Energy storage devices (e.g., batteries, fuel cells, ultracapacitors, etc.) have become significantly more prevalent in many government and commercial applications (e.g., automotive, military, space, electric utilities, medical, etc.). Consequently, there has also been an increased interest in smart monitoring systems that can effectively manage energy storage devices (ESDs) so as to optimize performance and extend life. An important aspect of these smart monitoring systems is the ability to estimate the response of an ESD to an anticipated load.
For example, the Lumped Parameter Model (LPM) has been used extensively by the Idaho National Laboratory (INL) to estimate the voltage response of a battery to a constant-current pulse for automotive applications. The LPM is an equivalent circuit model that recursively solves for the voltage behavior based on a given excitation current and a set of difference equations. It has been shown that the LPM is sensitive to variations in pulse amplitude and duration and could, therefore, be a useful measure of state-of-health (Christophersen et al., “Lumped Parameter Modeling as a Predictive Tool for a Battery Status Monitor,” Proceedings from 2003 IEEE Vehicular Technology Conference, October 2003), the content of which is hereby incorporated by reference in its entirety.
However, the excitation signals required to obtain estimates of the ESD response are not well suited for in-situ applications since it generally requires a pulse test, which may cause larger state-of-charge (SOC) swings than desired and even adversely affect the ESD (Christophersen et al., “Effects of Reference Performance Testing During Aging Using Commercial Lithium-Ion Cells,” J. Electrochem. Soc., 153, A1406-A1416, 2006), the content of which is hereby incorporated by reference in its entirety. A need still exists to estimate the response of an ESD to an anticipated load using benign measurement techniques.
The INL has also shown that the pulse resistance for batteries is strongly correlated with the growth observed from corresponding electrochemical impedance spectroscopy (EIS) measurements (Christophersen et al., “Electrochemical Impedance Spectroscopy Testing on the Advanced Technology Development Program Lithium-Ion Cells,” IEEE Trans. Veh. Technol., 56 (3), 1851-1855, 2002), the content of which is hereby incorporated by reference in its entirety. It has also been shown that EIS techniques are more benign than pulse tests (Christophersen et al., “Effects of Reference Performance Testing During Aging Using Commercial Lithium-Ion Cells,” J. Electrochem. Soc., 153, A1406-A1416, 2006) since it is a low-level, charge neutral signal that minimally perturbs the ESD. Suitable means for obtaining in-situ impedance spectra have already been developed. The Impedance Noise Identification method (U.S. Pat. No. 7,675,293), the content of which is hereby incorporated by reference in its entirety) uses a random signal excitation to acquire a high-resolution impedance spectrum, but at the expense of computationally intensive data processing. An alternative approach is known as “Compensated Synchronous Detection” (U.S. Pat. No. 7,395,163), the content of which is hereby incorporated by reference in its entirety), and it incorporates a wideband sum-of-sines (SOS) input signal to measure the impedance. It yields a faster measurement, but at the expense of lower resolution. A variant of Compensated Synchronous Detection is Fast Summation Transformation (FST). The principal attributes of FST (Morrison et al., “Fast Summation Transformation for Battery Impedance Identification,” IEEE Aerospace 2009 Conference, March 7-14, Big Sky, Mont., the content of which is hereby incorporated by reference in its entirety) are that it only requires one period of the lowest frequency to complete the measurement, and the data processing algorithm is very simple.